SMILE Safety analysis and verification/validation of MachIne LEarning based systems

Reference number
Funding from Vinnova SEK 500 000
Project duration November 2016 - September 2017
Status Completed
Venture Electronics, software and communication - FFI
End-of-project report 2016-04255.pdf(pdf, 432 kB) (In Swedish)

Purpose and goal

The purpose of SMILE is to explore the challenges while introducing machine learning-based systems in automated driving (AD) applications. It also aims to propose strategies to cope with those challenges to guide the industry and thus, be able to realize the potential to apply machine learning in safety critical systems.

Expected results and effects

SMILE resulted in a research agenda for a research program also named SMILE and an application for a continuation project. The continuation project aims at developing run-time monitoring for Deep Machine Learning (DML)-based perception using the concept of adaptive safety cage architectures a.k.a. safety supervisors, as a strategy to increasing the integrity towards faults when using DML in AD. An application for an Institute PhD student project within the area is also a result of the project. The project findings are published in two papers and two poster presentations.

Planned approach and implementation

The project was brought out in two parallel tracks, a theoretical state-of-art and a workshop series, and finalized with an analysis/synthesis. The theoretical framework of the industrial needs were found during the analysis formulated into a research agenda for the SMILE research program.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 25 November 2019

Reference number 2016-04255

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